November 2016 Blog Posts (95)

As the reach of social data expands by every passing day, more and more people are posting their views, thoughts and ideas over a broad array of interconnected social networks. In the era of the booming social media it is critical for businesses to sieve through social data - data generated through the multiple social media channels to gather the actionable insights.

E-Commerce has been on the rise with more and more businesses taking to online platforms to reach out to their clientele. Consequently, there has been a significant increase in the number of online spending, which has been due to the increase in the adoption and ownership of mobile devices that has resulted in a shift to the web. As a result, shoppers are always looking out for unmatched shopping experiences. That is, quick access, round-the-clock availability, accessible content and smooth…

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back.

I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related…

This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It's a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were used in the Matrices, same color as the Neural Network structure (bias, input, hidden, output) to make it easier to understand.…

These 19 'sets of data sets' cover free or public data from various industries, including small and large, structured and unstructured data sets. Hone your data science and machine learning skills on these data sets, or use them for testing algorithms or for benchmarking.

In this post I want to talk about all of us finding our place and building our careers in data. And when I say “data”, I mean analytics, data science, business intelligence, and so on. In the previous post, I talked…

We may be years away from the “AI-enabled Coworker,” but the first implementations of machine-learning capabilities are finding their way into the everyday data-analysis tools used by businesses of all types. Cognitive assistance promises to reshape business processes, but only if app development and deployment tools are adapted to support machine learning.

While it has become fashionable to hypeAIas the next game-changing technology promising to have an impact greater than either…

Summary: IBM’s Watson as it exists today is as close as we’ve come to a single integrated platform for AI. It contains all the capabilities for image and video, natural language speech and text input and output, and the most comprehensive knowledge recovery module yet combined together. If you want to exploit the advances we’ve made in AI you need to understand where Watson is today and where it’s heading.

To produce a regression analysis of inference that can be justified or trustworthy in the sense that helpful. The term in the statistical methods that generate a linear the best estimator is not bias (best linear unbiased estimator) abbreviated BLUE. Then there are some other things that are also important to note, in which the data to be processed, must meet certain requirements. In terms of statistical methods some terms or conditions of the so-called classical assumption test. Because…

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be a good result – but is it really? Should we consider a model with 93% accuracy as adequate?

Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment. This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual…

A number of my mid-sized European clients recently have asked me to help them scope and recruit for the role of Chief Data Scientist (also scoped as ‘Head of Data Science’, ‘Lead Data Scientist’, ‘Head of Analytics’, etc.).

This lead data science role is typically opened by the client company for one of two…